Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r2 = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Model cortical association fields account for the time course and dependence on target complexity of human contour perception.

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least [Formula: see text] ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

A simple algorithm for estimation of source-to-detector distance in Compton imaging.

Compton imaging is used to predict the location of gamma-emitting radiation sources. The X and Y coordinates of the source can be obtained using a back-projected image and a two-dimensional peak-finding algorithm. The emphasis of this work is to estimate the source-to-detector distance (Z). The algorithm presented uses the solid angle subtended by the reconstructed image at various source-to-detector distances. This algorithm was validated using both measured data from the prototype Compton imager (PCI) constructed at the Los Alamos National Laboratory and simulated data of the same imager. Results show this method can be applied successfully to estimate Z, and it provides a way of determining Z without prior knowledge of the source location. This method is faster than the methods that employ maximum likelihood method because it is based on simple back projections of Compton scatter data.